Gabor Rudolf

Distributionally Robust Optimization under Decision-Dependent Ambiguity Set with an Application to Machine Scheduling

  • Miguel A. Lejeune
  • Nilay Noyan
  • Gabor Rudolf
    • Categories Scheduling, Stochastic Programming

    We introduce a new class of distributionally robust optimization problems under decision dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover’s distances that includes both the total variation distance and the Wasserstein metrics. We discuss the … Read more

    Distributionally Robust Optimization with Decision-Dependent Ambiguity Set

  • Miguel A. Lejeune
  • Nilay Noyan
  • Gabor Rudolf
    • Categories Stochastic Programming

    We introduce a new class of distributionally robust optimization problems under decision-dependent ambiguity sets. In particular, as our ambiguity sets we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover’s distances that includes both the total variation distance and the Wasserstein metrics. We discuss the main … Read more

    Optimization with stochastic preferences based on a general class of scalarization functions

  • Nilay Noyan
  • Gabor Rudolf
    • Categories Multi-Criteria Optimization, Stochastic Programming

    It is of crucial importance to develop risk-averse models for multicriteria decision making under uncertainty. A major stream of the related literature studies optimization problems that feature multivariate stochastic benchmarking constraints. These problems typically involve a univariate stochastic preference relation, often based on stochastic dominance or a coherent risk measure such as conditional value-at-risk (CVaR), … Read more

    Kusuoka Representations of Coherent Risk Measures in General Probability Spaces

  • Nilay Noyan
  • Gabor Rudolf
    • Categories Stochastic Programming

    Kusuoka representations provide an important and useful characterization of law invariant coherent risk measures in atomless probability spaces. However, the applicability of these results is limited by the fact that such representations do not always exist in probability spaces with atoms, such as finite probability spaces. We introduce the class of functionally coherent risk measures, … Read more

    Optimization with multivariate conditional value-at-risk constraints

  • Nilay Noyan
  • Gabor Rudolf
    • Categories Applications - OR and Management Sciences, Multi-Criteria Optimization, Stochastic Programming

    For many decision making problems under uncertainty, it is crucial to develop risk-averse models and specify the decision makers’ risk preferences based on multiple stochastic performance measures (or criteria). Incorporating such multivariate preference rules into optimization models is a fairly recent research area. Existing studies focus on extending univariate stochastic dominance rules to the multivariate … Read more